The main goal of this project is to combine experimental and computational approaches to develop a detailed understanding of how the biophysical properties of individual retinal neurons and synapses shape the parallel processing of visual information during night vision. This project is a collaboration between two neurobiologists and two applied mathematicians. We will characterize experimentally and model computationally the neural circuitry of the mammalian retina that subserves night (scotopic) vision. The basic circuitry is well-described: photons absorbed by rod photoreceptors generate neural signals that are distributed to multiple types of retinal ganglion cells (GCs), the output cells of the retina, via a series of interneurons. These interneurons signal each other via chemical and electrical synapses. Each GC type has a unique light response, which is presumed to reflect the properties of its unique presynaptic circuitry. This circuitry enables each GC type to encode a unique feature of the visual scene, thereby facilitating further abstractions by higher brain areas that ultimately guide behavior. By characterizing and carefully modeling each component, by assembling the components into a comprehensive model, and by validating and refining the overall model experimentally, we will determine how diverse GC outputs emerge from the biophysical properties of parallel retinal microcircuits.Relevance to public health: Many retinal pathologies cause photoreceptor (rod and cone) death, which deprives GCs of their normal inputs. Recently, optogenetic approaches have been developed with the ultimate goal of restoring vision in the absence of phoptoreceptor function by making retinal neurons directly responsive to light. Here, we hope to identify key neurons to which photosensitivity may be endowed to permit the restoration of the ability to encode over a wide dynamic range the most relevant features of the visual world. The aims of this project address two explicit goals of the Retinal Diseases Program in the National Plan for Eye and Vision Research: 1) Increasing understanding of post-photoreceptor adaptation (i.e., gain control in neural circuits) and 2) Increasing understanding of how retinal cellular interactions within neural networks generate signals that are interpretable as visual images.